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陆军军医大学(第三军医大学)第一附属医院神经外科,重庆
鲜继淑, E-mail: xianjishu@tmmu.edu.cn
收稿日期:2024-10-21,
修回日期:2024-11-18,
纸质出版日期:2025-02-28
移动端阅览
张燕春,刘艳,汪睿,等.基于睡眠期体动特征的无感睡眠监测床垫的应用效果评价[J].陆军军医大学学报,2025,47(04):326-334.
ZHANG Yanchun,LIU Yan,WANG Rui,,et al.Application effect of a non-contact sleep monitoring mattress based on body movement characteristics during sleep[J].J Army Med Univ,2025,47(04):326-334.
张燕春,刘艳,汪睿,等.基于睡眠期体动特征的无感睡眠监测床垫的应用效果评价[J].陆军军医大学学报,2025,47(04):326-334. DOI:10.16016/j.2097-0927.202410073.
ZHANG Yanchun,LIU Yan,WANG Rui,,et al.Application effect of a non-contact sleep monitoring mattress based on body movement characteristics during sleep[J].J Army Med Univ,2025,47(04):326-334. DOI:10.16016/j.2097-0927.202410073.
目的
2
验证基于睡眠期体动特征的无感睡眠监测床垫(non-contact sleep monitoring mattress,NCSMM)在神经外科术前患者睡眠质量评估中的准确性,旨在为临床医护人员提供更加便携有效的评估工具。
方法
2
采用单臂研究设计,便利抽样法抽取陆军军医大学(第三军医大学)第一附属医院神经外科住院患者114名,通过NCSMM、多导睡眠图(polysomnography,PSG)、患者报告结局测量信息系统(patient-reported outcomes measurement information system,PROMIS)睡眠障碍简表、理查兹-坎贝尔睡眠量表(Richards-Campbell sleep questionnaire,RCSQ)以及可穿戴设备手表共5种睡眠质量评估工具进行一晚的睡眠监测。以PSG监测的睡眠效率(≤85%)为诊断睡眠障碍的依据,计算NCSMM、PROMIS、ROSQ、可穿戴设备手表受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under the curve,AUC)、敏感度、特异度、阳性预测率、阴性预测率及Youden指数等指标,比较各工具的评估效果。
结果
2
NCSMM、PROMIS、RCSQ、可穿戴设备手表的AUC分别为0.788[95%
CI
(0.687,0.888),
P
<
0.001]、0.664[95%
CI
(0.543,0.784),
P
=0.020]、0.723[95%
CI
(0.600,0.846),
P
=0.001]、0.750[95%
CI
(0.654,0.846),
P
<
0.001],诊断准确率分别为0.774、0.559、0.742、0.602,Youden指数分别为0.488、0.321、0.456、0.459。NCSMM的AUC、诊断准确率、Youden指数均优于其他3种工具。
结论
2
NCSMM在神经外科术前住院患者睡眠质量评估中表现出一定的的准确性,可作为便携有效的评估工具。
Objective
2
To verify the accuracy of a Non-Contact Sleep Monitoring Mattress (NCSMM) based on body movement during sleep in assessing sleep quality of patients before neurosurgery in order to provide a more portable and efficient assessment tool for clinical staff.
Methods
2
A single-arm trial was conducted on 114 inpatients admitted in our department selected with convenience sampling. Sleep quality data of 1 night were collected through 5 sleep quality assessment tools
including NCSMM
polysomnography (PSG)
Patient-Reported Outcome Measurement Information System (PROMIS) Sleep Disturbance scale
Richards-Campbell Sleep Scale (RCSQ)
and a wearable device (smart watch for body movements and sleep quality monitoring). The sleep efficiency (≤85%) obtained by PSG was used as the diagnostic standard for sleep disorders. The area under the receiver operating characteristic curve (AUC)
sensitivity
specificity
positive predictive value
negative predictive value
and Youden index were calculated for the other 4 tools to evaluate and compare their diagnostic effectiveness.
Results
2
The AUC value for NCSMM
PROMIS
RCSQ and smart watch was 0.788 (95%
CI
: 0.687~0.888
P
<
0.001)
0.664 (95%
CI
: 0.543~0.784
P
=0.02)
0.723 (95%
CI
: 0.600~0.846
P
=0.001) and 0.750 (95%
CI
: 0.654~0.846
P
<
0.001)
respectively. The diagnostic accuracy rate was 0.774
0.559
0.742 and 0.602
with corresponding Youden index value of 0.488
0.321
0.456
and 0.459. NCSMM demonstrated the best AUC value
sensitivity and Youden index when compared with the other 3 tools.
Conclusion
2
NCSMM shows high accuracy in assessing sleep quality in pre-neurosurgery inpatients
and it is a viable portable and efficient assessment tool in clinical practice.
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